Exploring sentence informativeness
This study is a preliminary exploration of the concept of informativeness -how much information a sentence gives about a word it contains- and its potential benefits to building quality word representations from scarce data. We propose several sentence-level classifiers to predict informativeness, and we perform a manual annotation on a set of sentences. We conclude that these two measures correspond to different notions of informativeness. However, our experiments show that using the classifiers' predictions to train word embeddings has an impact on embedding quality.
Code (0)
등록된 구현이 없습니다.
Tasks
InformativenessSentenceWord EmbeddingsSimilar Papers 제목 키워드 기반
SQUINKY! A Corpus of Sentence-level Formality, Informativeness, and Implicature
We introduce a corpus of 7,032 sentences rated by human annotators for formality, informativeness, and implicature on a 1-7 scale. The corpus was annotated using Amazon Mechanical Turk. Reliability in the obtained judgme…
InformativenessSentenceDoes Informativeness Matter? Active Learning for Educational Dialogue Act Classification
Dialogue Acts (DAs) can be used to explain what expert tutors do and what students know during the tutoring process. Most empirical studies adopt the random sampling method to obtain sentence samples for manual annotatio…
Active LearningDialogue Act ClassificationInformativenessSentenceExploring Text Links for Coherent Multi-Document Summarization
Summarization aims to represent source documents by a shortened passage. Existing methods focus on the extraction of key information, but often neglect coherence. Hence the generated summaries suffer from a lack of reada…
Document SummarizationInformativenessMulti-Document SummarizationText Generation`Keep it Together': Enforcing Cohesion in Extractive Summaries by Simulating Human Memory
Extractive summaries are usually presented as lists of sentences with no expected cohesion between them. In this paper, we aim to enforce cohesion whilst controlling for informativeness and redundancy in summaries, in ca…
InformativenessSentenceAn Attention-Based Model for Predicting Contextual Informativeness and Curriculum Learning Applications
Both humans and machines learn the meaning of unknown words through contextual information in a sentence, but not all contexts are equally helpful for learning. We introduce an effective method for capturing the level of…
InformativenessSentence